GIA: A Reusable General Interposer Architecture for Agile Chiplet Integration

Fuping Li, Ying Wang, Yuanqing Cheng, Yujie Wang, Yinhe Han, Huawei Li, Xiaowei Li
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引用次数: 2

Abstract

2.5D chiplet technology is gaining popularity for the efficiency of integrating multiple heterogeneous dies or chiplets on interposers, and it is also considered an ideal option for agile silicon system design by mitigating the huge design, verification, and manufacturing overhead of monolithic SoCs. Although it significantly reduces development costs by chiplet reuse, the design and fabrication of interposers also introduce additional high non-recurring engineering (NRE) costs and development cycles which might be prohibitive for application-specific designs having low volume.To address this challenge, in this paper, we propose a reusable general interposer architecture (GIA) to amortize NRE costs and accelerate integration flows of interposers across different chiplet-based systems effectively. The proposed assembly-time configurable interposer architecture covers both active interposers and passive interposers considering diverse applications of 2.5D systems. The agile interposer integration is also facilitated by a novel end-to-end design automation framework to generate optimal system assembly configurations including the selection of chiplets, inter-chiplet network configuration, placement of chiplets, and mapping on GIA, which are specialized for the given target workload. The experimental results show that our proposed active GIA and passive GIA achieve 3.15x and 60.92x performance boost with 2.57x and 2.99x power saving over baselines respectively.
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GIA:用于敏捷芯片集成的可重用通用中间层体系结构
2.5D晶片技术因其在中间体上集成多个异构芯片或晶片的效率而越来越受欢迎,并且通过减轻单片soc的巨大设计、验证和制造开销,它也被认为是敏捷硅系统设计的理想选择。虽然通过芯片的重复使用可以显著降低开发成本,但中间体的设计和制造也会引入额外的高非重复工程(NRE)成本和开发周期,这对于小批量的特定应用设计来说可能是令人望而却步的。为了解决这一挑战,在本文中,我们提出了一个可重用的通用中间层架构(GIA)来分摊NRE成本,并有效地加速跨不同基于芯片的系统的中间层集成流程。考虑到2.5D系统的不同应用,所提出的装配时可配置中介器架构涵盖了主动中介器和被动中介器。灵活的中间体集成还通过一个新颖的端到端设计自动化框架来促进,以生成最佳的系统装配配置,包括小芯片的选择、小芯片间网络配置、小芯片的放置和在GIA上的映射,这些都是专门针对给定的目标工作负载的。实验结果表明,我们提出的有源GIA和无源GIA在基准上分别实现了3.15倍和60.92倍的性能提升,分别节省2.57倍和2.99倍的功耗。
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